The invention of the computer and the development of the Internet has ushered in an era frequently referred to as the “Information Age” in which information is traded as a commodity and used in ways previously unimaginable. The low cost for storage and transmission of data means that all of the details of numerous daily transactions can be recorded and subsequently analyzed. These transactions are not limited to financial transactions—although those records are extremely revealing and may in some sense be the most important records—but can also include other aspects of personal and corporate life including communications, transportation and health.
The value of information is increasing as new ways to utilize that asset are developed. In many instances, a company's ability to effectively manage its data assets can determine the company's long-term viability. Companies that are inefficient in managing their acquired data assets find themselves at a competitive disadvantage with respect to other firms that have developed the expertise to fully exploit their information. As an example, retail stores that have been able to effectively mine and utilize their data to better understand and predict customer behavior have consistently been able to seize market share from their less agile competitors.
Mining of data can involve looking for a specific event, typically defined by single parameter or a small number of parameters related to a specific condition. As an example, a set of consumer data can be searched for credit transactions exceeding a threshold, or for an aggregate amount of transactions in a specified period of time exceeding a given threshold. Reporting these sets of transactions provides a simple means of identifying behaviors of interest. In the present example, it becomes possible to identify potential credit risks: individuals or corporations that have made single large transactions or sets of transactions in a specified time period that are substantially larger than ‘normal’ can be flagged as potential credit risks.
Nevertheless, in the prior art, tabulating occurrences indicative of a possible credit risk will not only produce a large number of false positives but can also potentially identify a large number of transactions and customers, with no indication as to how these transactions and customers are related to other events and entities that are indicative of the behavior of interest. Although the algorithms that can be applied to a dataset can be sophisticated and powerful and thus work to reduce the number of false positives, the behavior of interest typically has a complex definition and could not previously be identified by a computer.
We have found that it is desirable to have a system that allows the user to define an advanced scenario involving a number of aspects of the behavior of interest as applied to different lines of business and products, and to apply that definition of the advanced scenario to a dataset, producing alerts. The alerts should be presented to the user in a format that allows further investigation of different aspects of the behavior and filtering such that rapid pinpointing of individuals or corporations fitting the behavior of interest is possible.
For the foregoing reasons, there is a need for a method and a system that allows a user to define a behavior of interest, specifically describe aspects of that behavior related to both events and entities that are associated with that behavior, and subsequently monitor, on an ongoing basis, for the combinations of events and entities that are indicative of the behavior of interest.